With the increasing importance of personal identification for the purpose of security in remote transactions in today's world of electronic communication, biometric identification techniques are rapidly evolving into a pervasive method for personal verification. Among the different biometrics proposed for such a purpose, such as fingerprints, hand prints, voice prints, retinal images, handwriting samples and the like, fingerprint analysis is amongst the best studied biometric techniques. Fingerprint sensing and matching is a reliable and thus widely used practice for personal identification or verification. In a common approach to fingerprint identification, a live fingerprint is scanned and electronically digitized. The digitized data generally contains information pertaining to characteristic features of the fingerprint, such as ridge endings, points of ridge bifurcation, and the core of a whorl, i.e. fingerprint minutiae. The digitized data is then compared with stored data relating to fingerprints that have been obtained previously from corresponding authorized persons, i.e. fingerprint templates. When a match is detected, within a predetermined level of security in the form of a predetermined acceptance rate, the individual is identified and a corresponding action is performed.
In general, there are two types of errors associated with fingerprint identification. The first is a false reject or Type I error, and the second is a false accept or Type II error. A type II error occurs when there is sufficient similarity between fingerprints of two individuals that one is mistaken for the other. A Type I error occurs for a variety of reasons, and refers to when an individual is not identified even though the individual is an authorized user registered with the system.
It has been suggested that the underlying cause of errors in fingerprint analysis is that the amount of data from a fingerprint is too limited for it to be used in a biometric identification system involving a large number of users. Increasing resolution of the imaging devices to capture more detailed images of fingerprint minutiae, as well as the consideration of pore patterns of biological surfaces have both been applied to reduce the error rates which occur in fingerprint identification.
The above-mentioned causes for failures in fingerprint verification are closely related to the applied imaging and analyzing techniques, and are typically responsible for Type II errors. There are, however, other sources for identification errors, which are primarily of a non-biometric nature. As part of a human body, the finger and more particularly the skin is submitted to the same physiological basic rules as any other part of the human body. The skin has elastic properties that allow a certain degree of flexibility either in extending or in a constricting fashion. For example, in cold temperature conditions, the blood circulation in the extremities like fingers is reduced to maintain the body temperature. Conversely, in warmer temperature, the blood flow is increased. Thus, the condition of the fingertip and therefore the fingerprint profile itself may vary depending on the properties of the skin and the environmental conditions. This also implies slight modifications of the fingerprint to be characterized. Furthermore, the hygienic conditions of a hand, and more particularly that of the fingertip to be imaged, are also factors for possible interference in properly imaging a fingerprint. All this causes problems in a reproducibility of a fingerprint imaging process, and in turn leads to an increase in Type I or false reject error rates.
It is highly advantageous to provide a biometric imaging device capable of compensating for non-biometric parameters, by either providing well-defined conditions for imaging a biological surface, or by sensing and correcting for conditions prevalent during the process of imaging the biological surface. It is of further advantage to reduce the Type I error rates in a given fingerprint identification process, thus enhancing the reliability of fingerprint imaging devices.